Dataset Information

?7nAchR/NMDAR coupling affects NMDAR function and object recognition.

ABSTRACT: The ?7 nicotinic acetylcholine receptor (nAchR) and NMDA glutamate receptor (NMDAR) are both ligand-gated ion channels permeable to Ca2+ and Na+. Previous studies have demonstrated functional modulation of NMDARs by nAchRs, although the molecular mechanism remains largely unknown. We have previously reported that ?7nAchR forms a protein complex with the NMDAR through a protein-protein interaction. We also developed an interfering peptide that is able to disrupt the ?7nAchR-NMDAR complex and blocks cue-induced reinstatement of nicotine-seeking in rat models of relapse. In the present study, we investigated whether the ?7nAchR-NMDAR interaction is responsible for the functional modulation of NMDAR by ?7nAchR using both electrophysiological and behavioral tests. We have found that activation of ?7nAchR upregulates NMDAR-mediated whole cell currents and LTP of mEPSC in cultured hippocampal neurons, which can be abolished by the interfering peptide that disrupts the ?7nAchR-NMDAR interaction. Moreover, administration of the interfering peptide in mice impairs novel object recognition but not Morris water maze performance. Our results suggest that ?7nAchR/NMDAR coupling may selectively affect some aspects of learning and memory.

SUBMITTER: Li S

PROVIDER: S-EPMC3878138 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

α7nAchR/NMDAR coupling affects NMDAR function and object recognition.

Li Shupeng S Nai Qiang Q Lipina Tatiana V TV Roder John C JC Liu Fang F

Molecular brain 20131220

The α7 nicotinic acetylcholine receptor (nAchR) and NMDA glutamate receptor (NMDAR) are both ligand-gated ion channels permeable to Ca2+ and Na+. Previous studies have demonstrated functional modulation of NMDARs by nAchRs, although the molecular mechanism remains largely unknown. We have previously reported that α7nAchR forms a protein complex with the NMDAR through a protein-protein interaction. We also developed an interfering peptide that is able to disrupt the α7nAchR-NMDAR complex and bloc ...[more]

PMID: 24360204

Similar Datasets

Project description:Scene understanding and decomposition is a crucial challenge for intelligent systems, whether it is for object manipulation, navigation, or any other task. Although current machine and deep learning approaches for object detection and classification obtain high accuracy, they typically do not leverage interaction with the world and are limited to a set of objects seen during training. Humans on the other hand learn to recognize and classify different objects by actively engaging with them on first encounter. Moreover, recent theories in neuroscience suggest that cortical columns in the neocortex play an important role in this process, by building predictive models about objects in their reference frame. In this article, we present an enactive embodied agent that implements such a generative model for object interaction. For each object category, our system instantiates a deep neural network, called Cortical Column Network (CCN), that represents the object in its own reference frame by learning a generative model that predicts the expected transform in pixel space, given an action. The model parameters are optimized through the active inference paradigm, i.e., the minimization of variational free energy. When provided with a visual observation, an ensemble of CCNs each vote on their belief of observing that specific object category, yielding a potential object classification. In case the likelihood on the selected category is too low, the object is detected as an unknown category, and the agent has the ability to instantiate a novel CCN for this category. We validate our system in an simulated environment, where it needs to learn to discern multiple objects from the YCB dataset. We show that classification accuracy improves as an embodied agent can gather more evidence, and that it is able to learn about novel, previously unseen objects. Finally, we show that an agent driven through active inference can choose their actions to reach a preferred observation.

Dataset Information

?7nAchR/NMDAR coupling affects NMDAR function and object recognition.

Publications

α7nAchR/NMDAR coupling affects NMDAR function and object recognition.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets